GLM-5.3-Flash leads overall
Ahead on 2 of 5 benchmarks, averaging 3.1 points higher
Summarised from the 5 benchmarks both models were scored on; details in the charts below. A further 1 Elo/rating-scale benchmarks are left out of the average — their scale cannot be added to percentages.

Gemini 3.7 Flash
Google Deep Mind
Updates live with the mode filters below.
Best overall
GLM-5.3-Flash · 347.53
Best single
GLM-5.3-Flash · GDPval-AA v2 1773.00
Modality coverage
Gemini 3.7 Flash · 4 modalities
Head to head
6
Benchmarks
2
Wins
3
Losses
-43.95
Average diff
Benchmark-by-benchmark comparison. Changing the thinking mode or tool filters updates the chart and table below.
Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology
Each axis is the mean percentage score of one benchmark domain. It is an average, not a capability rating.
Relative edge: 编程与软件工程 +1.9 / Relative gap: AI Agent - 工具使用 -8.5
Relative edge: AI Agent - 工具使用 +8.5 / Relative gap: 编程与软件工程 -1.9
Method: for each model and benchmark, all scores in the current mode scope are averaged (not the best score), then those benchmark scores are averaged within each domain. Only benchmarks scored on a 0-100 scale by at least two of the selected models count — Elo and rating-scale benchmarks such as Codeforces or Arena are excluded, because averaging a 1500 rating with an 85% accuracy produces a meaningless number. Missing values are not counted as zero, and the averages are unweighted, so domains with harder benchmarks read lower.
Every model and runtime mode, benchmark by benchmark. Values are comparable along a row, not between different benchmarks.
6 benchmarks with comparable scores. Each model shows its best score; mode label is displayed below.
| Benchmark | Gemini 3.7 Flash | GLM-5.3-Flash |
|---|---|---|
AutomationBench AI Agent - 工具使用 | 30.40Thinking Enabled | Tools | 48.80Thinking Level · High | Tools |
Terminal-Bench 2.1 AI Agent - 工具使用 | 85.80Thinking Enabled | Tools | 84.30Thinking Level · High | Tools |
DeepSWE 编程与软件工程 | 65.30Thinking Level · High | Tools | 63.40Thinking Level · High | Tools |
Agents' Last Exam Agent能力评测 | 26.30Thinking Level · Medium | Tools | 26.30Thinking Level · High | Tools |
GDPval-AA v2 生产力知识 | 1525.00Thinking Enabled | 1773.00Thinking Level · High | Tools |
CharXiv RQ 多模态理解 | 88.70Thinking Level · Medium | Tools | 89.40Thinking Level · High | Tools |
Official list prices per model API, split by input and output. Unit: USD per 1M tokens.
Architecture, licensing and API modalities. "Not provided" means the field is missing from our database.
| Features & specs | Gemini 3.7 FlashGoogle Deep Mind | GLM-5.3-Flash智谱AI |
|---|---|---|
Core specsRelease | 2026-08-13 | 2026-08-26 |
Context length | 1M | 1M |
Total parameters | — | 320B |
Active parameters | N/A | 18B |
Max output length | 65,536 tokens | 128,000 tokens |
Architecture | Dense | MoE (mixture of experts) |
Runtime modes | 中低高 | 最高低高 |
LicenseCode Open Source | Closed Source | Open Source · MIT License |
Weights Open Source | Closed Source | Open Source · MIT License |
Licensing status | 不开源 | 免费商用授权 |
Local deploymentWeight size | Not provided | 约 328.3 GB(FP8 safetensors,62 个分片) |
VRAM for weights | Not provided | ≈ 328 GB (weights only, excludes KV cache) |
Weights | Not provided | Hugging Face |
Source repo | Not provided | GitHub |
API modality supportText Input/Output | / | / |
Image Input/Output | / | / |
Audio Input/Output | / | Not provided |
Video Input/Output | / | Not provided |
ResourcesPaper / report | Gemini 3.7 Flash | GLM-5: from Vibe Coding to Agentic Engineering |

GLM-5.3-Flash
智谱AI